The DKU-DukeECE Systems for VoxCeleb Speaker Recognition Challenge 2020
arXiv:2010.12731
Abstract
In this paper, we present the system submission for the VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20) by the DKU-DukeECE team. For track 1, we explore various kinds of state-of-the-art front-end extractors with different pooling layers and objective loss functions. For track 3, we employ an iterative framework for self-supervised speaker representation learning based on a deep neural network (DNN). For track 4, we investigate the whole system pipeline for speaker diarization, including voice activity detection (VAD), uniform segmentation, speaker embedding extraction, and clustering.
References in corpus (9)
- A Simple Framework for Contrastive Learning of Visual Representations
- VoxCeleb2: Deep Speaker Recognition
- VoxCeleb: a large-scale speaker identification dataset
- ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification
- Attentive Statistics Pooling for Deep Speaker Embedding
- The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF
- Spot the conversation: speaker diarisation in the wild
- A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
- The FFSVC 2020 Evaluation Plan
Cited by in corpus (5)
- VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge
- The SpeakIn System for VoxCeleb Speaker Recognition Challange 2021
- Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation
- The VoxCeleb Speaker Recognition Challenge: A Retrospective
- Towards Lightweight Speaker Verification via Adaptive Neural Network Quantization